Function Differences with tf.keras.optimizers.Ftrl

tf.keras.optimizers.Ftrl

tf.keras.optimizers.Ftrl(
    learning_rate=0.001,
    learning_rate_power=-0.5,
    initial_accumulator_value=0.1,
    l1_regularization_strength=0.0,
    l2_regularization_strength=0.0,
    name='Ftrl',
    l2_shrinkage_regularization_strength=0.0,
    beta=0.0,
    **kwargs) -> Tensor

For more information, see tf.keras.optimizers.Ftrl.

mindspore.nn.FTRL

class mindspore.nn.FTRL(
    params,
    initial_accum=0.1,
    learning_rate=0.001,
    lr_power=-0.5,
    l1=0.0,
    l2=0.0,
    use_locking=False,
    loss_scale=1.0,
    weight_decay=0.0)(grads) -> Tensor

For more information, see mindspore.nn.FTRL.

Differences

TensorFlow: An online convex optimization algorithm for shallow models with large and sparse feature feature spaces.

MindSpore: MindSpore API basically implements the same function as TensorFlow.

Categories	Subcategories	TensorFlow	MindSpore	Differences
Parameters	Parameter 1	learning_rate	learning_rate	-
	Parameter 2	learning_rate_power	lr_power	Same function, different parameter names
	Parameter 3	initial_accumulator_value	initial_accum	Same function, different parameter names
	Parameter 4	l1_regularization_strength	l1	Same function, different parameter names
	Parameter 5	l2_regularization_strength	l2	Same function, different parameter names
	Parameter 6	name	-	Not involved
	Parameter 7	l2_shrinkage_regularization_strength	weight_decay	Same function, different parameter names
	Parameter 8	beta	-	A floating point value, the default value is 0.0. MindSpore does not have this parameter.
	Parameter 9	**kwargs	-	Not involved
	Parameter 10	-	params	A list composed of Parameter or a list composed of dictionaries. TensorFlow does not have this parameter.
	Parameter 11	-	use_locking	If True, the update operation is protected with a lock, the default value is False. TensorFlow does not have this parameter.
	Parameter 12	-	loss_scale	Gradient scaling factor, default value: 1.0. TensorFlow does not have this parameter.
	Parameter 13	-	grads	Gradient of params in the optimizer. TensorFlow does not have this parameter.

Code Example 1

The learning_rate is set to 0.1. The two APIs achieve the same function and have the same usage.

# TensorFlow
import tensorflow as tf

opt = tf.keras.optimizers.Ftrl(learning_rate=0.1)
var = tf.Variable(1.0)
val0 = var.value()
loss = lambda: (var ** 2) / 2.0
step_count = opt.minimize(loss, [var]).numpy()
val1 = var.value()
print([val1.numpy()])
# [0.6031424]
step_count = opt.minimize(loss, [var]).numpy()
val2 = var.value()
print([val2.numpy()])
# [0.5532904]

# MindSpore
import numpy as np
import mindspore.nn as nn
import mindspore as ms
from mindspore.dataset import NumpySlicesDataset
from mindspore.train import Model

class Net(nn.Cell):
    def __init__(self):
        super(Net, self).__init__()
        self.w = ms.Parameter(ms.Tensor(np.array([1.0], np.float32)), name='w')
    def construct(self, x):
        f = self.w * x
        return f
class MyLoss(nn.LossBase):
    def __init__(self, reduction='none'):
        super(MyLoss, self).__init__()

    def construct(self, y, y_pred):
        return (y - y_pred) ** 2 / 2.0
net = Net()
loss = MyLoss()
optim = nn.FTRL(params=net.trainable_params(), learning_rate=0.1)
model = Model(net, loss_fn=loss, optimizer=optim)
data_x = np.array([1.0], dtype=np.float32)
data_y = np.array([0.0], dtype=np.float32)
data = NumpySlicesDataset((data_x, data_y), ["x", "y"])
input_x = ms.Tensor(np.array([1.0], np.float32))
y0 = net(input_x)
model.train(1, data)
y1 = net(input_x)
print(y1)
# [0.6031424]
model.train(1, data)
y2 = net(input_x)
print(y2)
# [0.5532904]

Code Example 2

The learning_rate is set to 0.01. The two APIs achieve the same function and have the same usage.

# TensorFlow
import tensorflow as tf

opt = tf.keras.optimizers.Ftrl(learning_rate=0.01)
var = tf.Variable(1.0)
val0 = var.value()
loss = lambda: (var ** 2) / 2.0
step_count = opt.minimize(loss, [var]).numpy()
val1 = var.value()
print([val1.numpy()])
# [0.688954]
step_count = opt.minimize(loss, [var]).numpy()
val2 = var.value()
print([val2.numpy()])
# [0.6834637]

# MindSpore
import numpy as np
import mindspore.nn as nn
import mindspore as ms
from mindspore.dataset import NumpySlicesDataset

class Net(nn.Cell):
    def __init__(self):
        super(Net, self).__init__()
        self.w = ms.Parameter(ms.Tensor(np.array([1.0], np.float32)), name='w')
    def construct(self, x):
        f = self.w * x
        return f
class MyLoss(nn.LossBase):
    def __init__(self, reduction='none'):
        super(MyLoss, self).__init__()

    def construct(self, y, y_pred):
        return (y - y_pred) ** 2 / 2.0
net = Net()
loss = MyLoss()
optim = nn.FTRL(params=net.trainable_params(), learning_rate=0.01)
model = ms.Model(net, loss_fn=loss, optimizer=optim)
data_x = np.array([1.0], dtype=np.float32)
data_y = np.array([0.0], dtype=np.float32)
data = NumpySlicesDataset((data_x, data_y), ["x", "y"])
input_x = ms.Tensor(np.array([1.0], np.float32))
y0 = net(input_x)
model.train(1, data)
y1 = net(input_x)
print(y1)
# [0.688954]
model.train(1, data)
y2 = net(input_x)
print(y2)
# [0.6834637]